Performance of saccade double-step and search-step tasks can be understood as the outcome of a race between GO processes initiating the alternative saccades and a STOP process (Camalier et al. 2007 Vision Res) paralleling the race model of stop signal task performance (Logan & Cowan 1984 Psych Rev). The models require stochastic independence of the finishing times of the racing processes. However, the control of movement initiation is accomplished by networks of neurons that interact through mutual inhibition. An interactive race model demonstrated how late, potent inhibition of a STOP unit on a GO unit can reproduce stop signal task performance with patterns of activation that resemble actual neural discharges (Boucher et al. 2007 Psych Rev). We are extending this interactive race architecture to account for double-step performance in which a second saccade is produced to the final target location after canceling the first saccade to the original target location. Alternative architectures have been explored. In a GO-STOP-GO architecture a separate STOP unit inhibits the GO unit producing the first saccade and allows the GO unit producing the second saccade to complete. In a GO-GO architecture the second GO unit both inhibits the first GO unit and initiates the saccade to the final target location.

The models were fit to the probability of compensating for the target step and the response times of compensated saccades as well as noncompensated followed by corrective saccades. The quality of fits of the GO-STOP-GO architecture was contrasted with that of the GO-GO architecture. Also, the form of the activation of the GO and STOP units was compared to the patterns of neural activity in FEF of monkeys performing the search step and double-step task (Murthy et al. 2009 J Neurophysiol). The results illustrate how stochastic cognitive models can be related to neural processes to understand how choice responses are initiated, interrupted and corrected.